This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Description
Monitoring vital physiological signals, such as heart rate, blood pressure and breathing pattern, are basic requirements in the diagnosis and management of various diseases. Traditionally, these signals are measured only in hospital and clinical settings. An important recent trend is the development of portable devices for tracking these physiological signals

Monitoring vital physiological signals, such as heart rate, blood pressure and breathing pattern, are basic requirements in the diagnosis and management of various diseases. Traditionally, these signals are measured only in hospital and clinical settings. An important recent trend is the development of portable devices for tracking these physiological signals non-invasively by using optical methods. These portable devices, when combined with cell phones, tablets or other mobile devices, provide a new opportunity for everyone to monitor one’s vital signs out of clinic.

This thesis work develops camera-based systems and algorithms to monitor several physiological waveforms and parameters, without having to bring the sensors in contact with a subject. Based on skin color change, photoplethysmogram (PPG) waveform is recorded, from which heart rate and pulse transit time are obtained. Using a dual-wavelength illumination and triggered camera control system, blood oxygen saturation level is captured. By monitoring shoulder movement using differential imaging processing method, respiratory information is acquired, including breathing rate and breathing volume. Ballistocardiogram (BCG) is obtained based on facial feature detection and motion tracking. Blood pressure is further calculated from simultaneously recorded PPG and BCG, based on the time difference between these two waveforms.

The developed methods have been validated by comparisons against reference devices and through pilot studies. All of the aforementioned measurements are conducted without any physical contact between sensors and subjects. The work presented herein provides alternative solutions to track one’s health and wellness under normal living condition.
ContributorsShao, Dangdang (Author) / Tao, Nongjian (Thesis advisor) / Li, Baoxin (Committee member) / Hekler, Eric (Committee member) / Karam, Lina (Committee member) / Arizona State University (Publisher)
Created2016
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Description
In brain imaging study, 3D surface-based algorithms may provide more advantages over volume-based methods, due to their sub-voxel accuracy to represent subtle subregional changes and solid mathematical foundations on which global shape analyses can be achieved on complicated topological structures, such as the convoluted cortical surfaces. On the other hand,

In brain imaging study, 3D surface-based algorithms may provide more advantages over volume-based methods, due to their sub-voxel accuracy to represent subtle subregional changes and solid mathematical foundations on which global shape analyses can be achieved on complicated topological structures, such as the convoluted cortical surfaces. On the other hand, given the enormous amount of data being generated daily, it is still challenging to develop effective and efficient surface-based methods to analyze brain shape morphometry. There are two major problems in surface-based shape analysis research: correspondence and similarity. This dissertation covers both topics by proposing novel surface registration and indexing algorithms based on conformal geometry for brain morphometry analysis.

First, I propose a surface fluid registration system, which extends the traditional image fluid registration to surfaces. With surface conformal parameterization, the complexity of the proposed registration formula has been greatly reduced, compared to prior methods. Inverse consistency is also incorporated to drive a symmetric correspondence between surfaces. After registration, the multivariate tensor-based morphometry (mTBM) is computed to measure local shape deformations. The algorithm was applied to study hippocampal atrophy associated with Alzheimer's disease (AD).

Next, I propose a ventricular surface registration algorithm based on hyperbolic Ricci flow, which computes a global conformal parameterization for each ventricular surface without introducing any singularity. Furthermore, in the parameter space, unique hyperbolic geodesic curves are introduced to guide consistent correspondences across subjects, a technique called geodesic curve lifting. Tensor-based morphometry (TBM) statistic is computed from the registration to measure shape changes. This algorithm was applied to study ventricular enlargement in mild cognitive impatient (MCI) converters.

Finally, a new shape index, the hyperbolic Wasserstein distance, is introduced. This algorithm computes the Wasserstein distance between general topological surfaces as a shape similarity measure of different surfaces. It is based on hyperbolic Ricci flow, hyperbolic harmonic map, and optimal mass transportation map, which is extended to hyperbolic space. This method fills a gap in the Wasserstein distance study, where prior work only dealt with images or genus-0 closed surfaces. The algorithm was applied in an AD vs. control cortical shape classification study and achieved promising accuracy rate.
ContributorsShi, Jie, Ph.D (Author) / Wang, Yalin (Thesis advisor) / Caselli, Richard (Committee member) / Li, Baoxin (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2016